Automated analysis of feature models: Quo vadis?

Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186

Approach to attributed feature modeling for requirements elicitation in Scrum agile development

Requirements elicitation is a core activity of requirements engineering for the product to be developed. The knowledge that has been gained during requirements engineering about the product to be developed forms the basis for requirement elicitation. The agile approach is becoming known day by day as the most widely used innovative process in the domain of requirements engineering. Requirements elicitation in agile development faces several challenges. Requirements must be gathered sufficiently to reflect stakeholders' needs. Furthermore, because of the development process, requirements evolve, and they must be adequately treated to keep up with the changing demands of the market and the passage of time. Another challenge with agile implementation is handling non-functional requirements in software development. Addressing non- functional requirements is still a critical factor in the success of any product. Requirements prioritization is also one of the most challenging tasks, and it is uncommon for requirement engineers to be able to specify and document all the requirements at once. This paper presents an approach for requirements elicitation in scrum-based agile development. The approach operates with the feature modeling technique, which is originally used in the Software Product Line (SPL). One of the most important proposed extensions to Feature Models (FMs) is the introduction of feature attributes. Our method uses attributed FMs to consider both functional and non-functional requirements as well as requirement prioritization. For the evaluation purposes, we have demonstrated our approach through two case studies in different domains of software product development. The first case study is in the domain of education, and the second one is in the domain of health care. The results reveal that our approach fits the requirements elicitation process in scrum agile development.Bourns College of Engineering, University of California, Riverside(undefined

Attribute-based variability in feature models

Extended feature models enable the expression of complex cross-tree constraints involving feature attributes. The inclusion of attributes in cross-tree relations not only enriches the constraints, but also engenders an extended type of variability that involves attributes. In this article, we elaborate on the effects of this new variability type on feature models. We start by analyzing the nature of the variability involving attributes and extend the definitions of the configuration and the product to suit the emerging requirements. Next, we propose classifications for the features, configurations, and products to identify and formalize the ramifications that arise due to the new type of variability. Then, we provide a semantic foundation grounded on constraint satisfaction for our proposal. We introduce an ordering relation between configurations and show that the set of all the configurations represented by a feature model forms a semilattice. This is followed by a demonstration of how the feature model analyses will be affected using illustrative examples selected from existing and novel analysis operations. Finally, we summarize our experiences, gained from a commercial research and development project that employs an extended feature model